






"""simple docstring"""





import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_SCREAMING_SNAKE_CASE   :							Any          =		'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		direct_transformers_import(PATH_TO_TRANSFORMERS)

_SCREAMING_SNAKE_CASE   :							Optional[int]          =		transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE   :							Tuple          =		{
    # used to compute the property `self.chunk_length`
    '''EncodecConfig''': ['''overlap'''],
    # used as `self.bert_model = BertModel(config, ...)`
    '''DPRConfig''': True,
    # not used in modeling files, but it's an important information
    '''FSMTConfig''': ['''langs'''],
    # used internally in the configuration class file
    '''GPTNeoConfig''': ['''attention_types'''],
    # used internally in the configuration class file
    '''EsmConfig''': ['''is_folding_model'''],
    # used during training (despite we don't have training script for these models yet)
    '''Mask2FormerConfig''': ['''ignore_value'''],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    '''OneFormerConfig''': ['''ignore_value''', '''norm'''],
    # used during preprocessing and collation, see `collating_graphormer.py`
    '''GraphormerConfig''': ['''spatial_pos_max'''],
    # used internally in the configuration class file
    '''T5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    '''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    '''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    # used internally in the configuration class file
    '''LongT5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    '''SwitchTransformersConfig''': ['''feed_forward_proj'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''BioGptConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''GLPNConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''SegformerConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''CvtConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''PerceiverConfig''': ['''layer_norm_eps'''],
    # used internally to calculate the feature size
    '''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate `mlp_dim`
    '''SamVisionConfig''': ['''mlp_ratio'''],
    # For (head) training, but so far not implemented
    '''ClapAudioConfig''': ['''num_classes'''],
    # Not used, but providing useful information to users
    '''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        '''CLIPSegConfig''': True,
        '''DeformableDetrConfig''': True,
        '''DetaConfig''': True,
        '''DinatConfig''': True,
        '''DonutSwinConfig''': True,
        '''EfficientFormerConfig''': True,
        '''FSMTConfig''': True,
        '''JukeboxConfig''': True,
        '''LayoutLMv2Config''': True,
        '''MaskFormerSwinConfig''': True,
        '''MT5Config''': True,
        '''NatConfig''': True,
        '''OneFormerConfig''': True,
        '''PerceiverConfig''': True,
        '''RagConfig''': True,
        '''SpeechT5Config''': True,
        '''SwinConfig''': True,
        '''Swin2SRConfig''': True,
        '''Swinv2Config''': True,
        '''SwitchTransformersConfig''': True,
        '''TableTransformerConfig''': True,
        '''TapasConfig''': True,
        '''TransfoXLConfig''': True,
        '''UniSpeechConfig''': True,
        '''UniSpeechSatConfig''': True,
        '''WavLMConfig''': True,
        '''WhisperConfig''': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        '''JukeboxPriorConfig''': True,
        # TODO: @Younes (for `is_decoder`)
        '''Pix2StructTextConfig''': True,
    }
)







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Union[str, Any]					) ->							List[Any]:
  lowerCamelCase_       	=		False
  for attribute in attributes:
    for modeling_source in source_strings:
      # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
      if (
          F'''config.{attribute}''' in modeling_source
          or F'''getattr(config, "{attribute}"''' in modeling_source
          or F'''getattr(self.config, "{attribute}"''' in modeling_source
      ):
        lowerCamelCase_       	=		True
      # Deal with multi-line cases
      elif (
          re.search(
              rF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''						,							_lowerCamelCase						,							)
          is not None
      ):
        lowerCamelCase_       	=		True
      # `SequenceSummary` is called with `SequenceSummary(config)`
      elif attribute in [
          "summary_type",
          "summary_use_proj",
          "summary_activation",
          "summary_last_dropout",
          "summary_proj_to_labels",
          "summary_first_dropout",
      ]:
        if "SequenceSummary" in modeling_source:
          lowerCamelCase_       	=		True
      if attribute_used:
        break
    if attribute_used:
      break

    # common and important attributes, even if they do not always appear in the modeling files
  lowerCamelCase_       	=		[
      'bos_index',
      'eos_index',
      'pad_index',
      'unk_index',
      'mask_index',
      'image_size',
      'use_cache',
      'out_features',
      'out_indices',
  ]
  lowerCamelCase_       	=		['encoder_no_repeat_ngram_size']

  # Special cases to be allowed
  lowerCamelCase_       	=		True
  if not attribute_used:
    lowerCamelCase_       	=		False
    for attribute in attributes:
      # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
      if attribute in ["is_encoder_decoder"] and default_value is True:
        lowerCamelCase_       	=		True
      elif attribute in ["tie_word_embeddings"] and default_value is False:
        lowerCamelCase_       	=		True

      # Allow cases without checking the default value in the configuration class
      elif attribute in attributes_to_allow + attributes_used_in_generation:
        lowerCamelCase_       	=		True
      elif attribute.endswith('_token_id'					):
        lowerCamelCase_       	=		True

      # configuration class specific cases
      if not case_allowed:
        lowerCamelCase_       	=		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__						,							[]					)
        lowerCamelCase_       	=		allowed_cases is True or attribute in allowed_cases

  return attribute_used or case_allowed







def 	lowerCamelCase__			(				_lowerCamelCase			:						int					) ->							List[Any]:
  lowerCamelCase_       	=		dict(inspect.signature(config_class.__init__					).parameters					)
  lowerCamelCase_       	=		[x for x in list(signature.keys()					) if x not in ['self', 'kwargs']]
  lowerCamelCase_       	=		[signature[param].default for param in parameter_names]

  # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
  # as one variant is used, the test should pass
  lowerCamelCase_       	=		{}
  if len(config_class.attribute_map					) > 0:
    lowerCamelCase_       	=		{v: k for k, v in config_class.attribute_map.items()}

  # Get the path to modeling source files
  lowerCamelCase_       	=		inspect.getsourcefile(_lowerCamelCase					)
  lowerCamelCase_       	=		os.path.dirname(_lowerCamelCase					)
  # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
  lowerCamelCase_       	=		[os.path.join(_lowerCamelCase						,							_lowerCamelCase					) for fn in os.listdir(_lowerCamelCase					) if fn.startswith('modeling_'					)]

  # Get the source code strings
  lowerCamelCase_       	=		[]
  for path in modeling_paths:
    if os.path.isfile(_lowerCamelCase					):
      with open(_lowerCamelCase					) as fp:
        modeling_sources.append(fp.read()					)

  lowerCamelCase_       	=		[]
  for config_param, default_value in zip(_lowerCamelCase						,							_lowerCamelCase					):
    # `attributes` here is all the variant names for `config_param`
    lowerCamelCase_       	=		[config_param]
    # some configuration classes have non-empty `attribute_map`, and both names could be used in the
    # corresponding modeling files. As long as one of them appears, it is fine.
    if config_param in reversed_attribute_map:
      attributes.append(reversed_attribute_map[config_param]					)

    if not check_attribute_being_used(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					):
      unused_attributes.append(attributes[0]					)

  return sorted(_lowerCamelCase					)







def 	lowerCamelCase__			(				) ->							Any:
  lowerCamelCase_       	=		{}
  for _config_class in list(CONFIG_MAPPING.values()					):
    # Skip deprecated models
    if "models.deprecated" in _config_class.__module__:
      continue
    # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
    lowerCamelCase_       	=		[
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class					)						,							lambda _lowerCamelCase					: inspect.isclass(_lowerCamelCase					)
            and issubclass(_lowerCamelCase						,							_lowerCamelCase					)
            and inspect.getmodule(_lowerCamelCase					) == inspect.getmodule(_config_class					)						,							)
    ]
    for config_class in config_classes_in_module:
      lowerCamelCase_       	=		check_config_attributes_being_used(_lowerCamelCase					)
      if len(_lowerCamelCase					) > 0:
        lowerCamelCase_       	=		unused_attributes

  if len(_lowerCamelCase					) > 0:
    lowerCamelCase_       	=		'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
    for name, attributes in configs_with_unused_attributes.items():
      error += F'''{name}: {attributes}\n'''

    raise ValueError(_lowerCamelCase					)


if __name__ == "__main__":
  check_config_attributes()












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




